DOI: 10.1002/2015JD023787
论文题名: A multivariate conditional model for streamflow prediction and spatial precipitation refinement
作者: Liu Z. ; Zhou P. ; Chen X. ; Guan Y.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2015
卷: 120, 期: 19 起始页码: 10116
结束页码: 10129
语种: 英语
英文关键词: conditional distribution
; spatial precipitation refinement
; streamflow prediction
; uncertainty estimation
; vine copula
Scopus关键词: fuzzy mathematics
; graphical method
; hydrometeorology
; model test
; multivariate analysis
; performance assessment
; precipitation assessment
; prediction
; regression analysis
; remote sensing
; soil moisture
; spatial analysis
; spatial distribution
; streamflow
; TRMM
; uncertainty analysis
; China
; Yangtze Basin
英文摘要: The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations. ©2015. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62996
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Institute of Geography, Heidelberg University, Heidelberg, Germany; Department of Forest Ecology, Guangdong Academy of Forestry, Guangzhou, China; South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China; College of Resources and Environment, State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest AandF University, Yangling, China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, China
Recommended Citation:
Liu Z.,Zhou P.,Chen X.,et al. A multivariate conditional model for streamflow prediction and spatial precipitation refinement[J]. Journal of Geophysical Research: Atmospheres,2015-01-01,120(19)